Bayesian methods for reaction mechanism reduction

Identifying the most important reaction mechanisms in hydrocarbon reaction networks remains a challenge for the computational chemistry community. Treating all possible intermediates with full-accuracy DFT is impossible, but these tools can be focused on the most interesting reactions. Bayesian techniques allow uncertainty to be propagated through these networks and through multiple levels of approximation and the most important reaction steps to be identified probabilistically. Current applications are large hydrocarbon reaction networks in thermal catalysis.